Dataset and Investigation Overview¶
This data set contains 113,937 loans with 81 variables on each loan, including loan amount, borrower rate (or interest rate), current loan status, borrower income, and many others. The analysis in this worksheet/notebook would be structured to enable us provide univariate, bivariate and multivariate relationships, this research would provide answers to questions like how many homeowners borrow in a state, which year has the highest rate of borrowing homeowners, which state has the highest amount of borrowing homeowners, is there a correlation between your available bank credit and your borrowing capacity, identifying the frequency of the categorical variables; Term of loan, Borrower's employment status, year of loan, and loan status, are there differences between loans depending on the loan term?. The data set consist of 113,937 rows and 81 columns, implying 113,937 recorded observations with 81 features. The main features of interest to this study include but not limited to the following; loan status, loan term, employment Status, is borrower a homeowner or not, borrower state, income verifiable or not and occupation. To get a better understanding of how this features of interest would be investigated a number of features would support this study which include the following features original loan amount, loan origination date, monthly loan payment, loan current days of delinquency, stated monthly income, investors and recommendations. In total 11 features were pulled together and form into a new dataframe to be reference for exploration and analysis. To analyse the loan with respect the year, the loan origination date column was converted from object datatype to datetime, afterwards the year was extracted from the datetime before setting the data type of the extracted year column as categorical variable, also the loan term values was trasform from the original values 12months, 36months, and 60months to short term, medium term and long term respectively to make for a better behavior as a categorical variable. The loan status has values respresenting past due in a number of categories of days, these values were replaced with a single value named 'past due' regardless of the number of days. The borrower state values were transformed from state abbrevation to full text without leaving out the stated monthy income and monthy loan payment variable out from transformation, these variables were converted from float to integer for consistency with the loan amount data type. The occupation column was transformed from object data type to categorical data type
Histogram Distibution of Loan Original Amount¶
The distribution of loan original amount is right-skewed, a case of symmetrical distribution. Most of the loan original amount are clustered on the left side of the histogram. The peak of the original loan amount occurs at about 5000 dollars, there exist outliers in the ranges between 32000 dollars and 35000 dollars, the data spread is from about 1000 dollars to 3500 dollars.
Histogram Distibution of Monthly Loan Payment¶
The monthly loan payment is also right-skewed, a case of symmetrical distribution. Most of the monthly loan payment are clustered on the left side of the histogram. The peak of the original loan amount occurs at about 173 dollars, the data spread is from about zero dollars to 2251 dollars.
DENSITY FUNCTION OF SOME DATA POINTS
The probability that a randomly chosen loan original amount will fall between 5000 dollars and 12000 dollars can be calculated as the area between the density function (graph) and the x-axis in the interval [5000, 12000].
Kernel Density Estimate for Monthly Loan Payment¶
The probability that a randomly chosen monthly loan payment will fall between 300 dollars and 500 dollars can be calculated as the area between the density function (graph) and the x-axis in the interval [300, 500]
Term of Loan Distribution¶
The probability that a randomly chosen monthly loan payment will fall between 300 dollars and 500 dollars can be calculated as the area between the density function (graph) and the x-axis in the interval [300, 500]
Borrower's Employment Status Distribution¶
From the above graph, we can infer that those who are employed are the highest borrowers, does this mean the emploees are not getting enough money that suits their lifestlye? or the loan company prefers giving out loans to working class individuals?
Loan Distribution by Year¶
Year 2013 spiked up in number of loan disbursed, before dropping back drastically in 2014 what might cause this? Economic crisis? Inflation?
Is Borrowere a home owner or not?¶
The difference between a homeowner and a non homeowner who borrowed is not that specific, but its worth noting that we still tried to find out
Line Graph Depicting Relationship Between Monthly Loan Payment and Loan Original Amount¶
There is no positive correlation between the two variables as the figure above shows that majority of the values fall between 0 and 35000 dollars
Line Plot Depicting Relationship Between Monthly Loan Payment and Loan Original Amount¶
As the original loan amount increases the monthly loan payment also increases, meaning there is a rush to pay off the loan as quickly as possible which means, there is a positive correlation between our two variables.
Original Loan Amount Against Current Days of Delinquency Group by Loan Term¶
There exists a positive relationship between the loan original amount and monthly loan payment, the data point(s) are spread across the plot categorized by term of loan.
Original Loan Amount Against Current Days of Delinquency Group by Loan Year¶
Original Loan Amount Against Current Days of Delinquency Group by Loan Year
Original Loan Amount Against Current Days of Delinquency Group Employment Status¶
From earlier findings there exist a positive relationship between the loan original amount and monthly loan payment, the data point are spread across the plot categorized by year of loan.
Correlation Matrix Depicting Relationship Between Variable with Heatmap¶
Correlation between numerical variables based on their linear properties by plotting a heatmap. We can deduce from the heatmap that, we have a positive correlation between loan original amount and monthly loan payment with a correlation coefficient of 0.93, the stated monthly income and the monthly loan payment seems to have no correlation between them as well as the income verifiable and the Loans Current Days deliquent column.
Text(0.5, 1.0, 'Correlation Matrix Depicting Relationship Between Variable with Heatmap.')
This application is used to convert notebook files (*.ipynb)
to various other formats.
WARNING: THE COMMANDLINE INTERFACE MAY CHANGE IN FUTURE RELEASES.
Options
=======
The options below are convenience aliases to configurable class-options,
as listed in the "Equivalent to" description-line of the aliases.
To see all configurable class-options for some <cmd>, use:
<cmd> --help-all
--debug
set log level to logging.DEBUG (maximize logging output)
Equivalent to: [--Application.log_level=10]
--show-config
Show the application's configuration (human-readable format)
Equivalent to: [--Application.show_config=True]
--show-config-json
Show the application's configuration (json format)
Equivalent to: [--Application.show_config_json=True]
--generate-config
generate default config file
Equivalent to: [--JupyterApp.generate_config=True]
-y
Answer yes to any questions instead of prompting.
Equivalent to: [--JupyterApp.answer_yes=True]
--execute
Execute the notebook prior to export.
Equivalent to: [--ExecutePreprocessor.enabled=True]
--allow-errors
Continue notebook execution even if one of the cells throws an error and include the error message in the cell output (the default behaviour is to abort conversion). This flag is only relevant if '--execute' was specified, too.
Equivalent to: [--ExecutePreprocessor.allow_errors=True]
--stdin
read a single notebook file from stdin. Write the resulting notebook with default basename 'notebook.*'
Equivalent to: [--NbConvertApp.from_stdin=True]
--stdout
Write notebook output to stdout instead of files.
Equivalent to: [--NbConvertApp.writer_class=StdoutWriter]
--inplace
Run nbconvert in place, overwriting the existing notebook (only
relevant when converting to notebook format)
Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory=]
--clear-output
Clear output of current file and save in place,
overwriting the existing notebook.
Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory= --ClearOutputPreprocessor.enabled=True]
--no-prompt
Exclude input and output prompts from converted document.
Equivalent to: [--TemplateExporter.exclude_input_prompt=True --TemplateExporter.exclude_output_prompt=True]
--no-input
Exclude input cells and output prompts from converted document.
This mode is ideal for generating code-free reports.
Equivalent to: [--TemplateExporter.exclude_output_prompt=True --TemplateExporter.exclude_input=True --TemplateExporter.exclude_input_prompt=True]
--allow-chromium-download
Whether to allow downloading chromium if no suitable version is found on the system.
Equivalent to: [--WebPDFExporter.allow_chromium_download=True]
--disable-chromium-sandbox
Disable chromium security sandbox when converting to PDF..
Equivalent to: [--WebPDFExporter.disable_sandbox=True]
--show-input
Shows code input. This flag is only useful for dejavu users.
Equivalent to: [--TemplateExporter.exclude_input=False]
--embed-images
Embed the images as base64 dataurls in the output. This flag is only useful for the HTML/WebPDF/Slides exports.
Equivalent to: [--HTMLExporter.embed_images=True]
--log-level=<Enum>
Set the log level by value or name.
Choices: any of [0, 10, 20, 30, 40, 50, 'DEBUG', 'INFO', 'WARN', 'ERROR', 'CRITICAL']
Default: 30
Equivalent to: [--Application.log_level]
--config=<Unicode>
Full path of a config file.
Default: ''
Equivalent to: [--JupyterApp.config_file]
--to=<Unicode>
The export format to be used, either one of the built-in formats
['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'rst', 'script', 'slides', 'webpdf']
or a dotted object name that represents the import path for an
``Exporter`` class
Default: ''
Equivalent to: [--NbConvertApp.export_format]
--template=<Unicode>
Name of the template to use
Default: ''
Equivalent to: [--TemplateExporter.template_name]
--template-file=<Unicode>
Name of the template file to use
Default: None
Equivalent to: [--TemplateExporter.template_file]
--theme=<Unicode>
Template specific theme(e.g. the name of a JupyterLab CSS theme distributed
as prebuilt extension for the lab template)
Default: 'light'
Equivalent to: [--HTMLExporter.theme]
--writer=<DottedObjectName>
Writer class used to write the
results of the conversion
Default: 'FilesWriter'
Equivalent to: [--NbConvertApp.writer_class]
--post=<DottedOrNone>
PostProcessor class used to write the
results of the conversion
Default: ''
Equivalent to: [--NbConvertApp.postprocessor_class]
--output=<Unicode>
overwrite base name use for output files.
can only be used when converting one notebook at a time.
Default: ''
Equivalent to: [--NbConvertApp.output_base]
--output-dir=<Unicode>
Directory to write output(s) to. Defaults
to output to the directory of each notebook. To recover
previous default behaviour (outputting to the current
working directory) use . as the flag value.
Default: ''
Equivalent to: [--FilesWriter.build_directory]
--reveal-prefix=<Unicode>
The URL prefix for reveal.js (version 3.x).
This defaults to the reveal CDN, but can be any url pointing to a copy
of reveal.js.
For speaker notes to work, this must be a relative path to a local
copy of reveal.js: e.g., "reveal.js".
If a relative path is given, it must be a subdirectory of the
current directory (from which the server is run).
See the usage documentation
(https://nbconvert.readthedocs.io/en/latest/usage.html#reveal-js-html-slideshow)
for more details.
Default: ''
Equivalent to: [--SlidesExporter.reveal_url_prefix]
--nbformat=<Enum>
The nbformat version to write.
Use this to downgrade notebooks.
Choices: any of [1, 2, 3, 4]
Default: 4
Equivalent to: [--NotebookExporter.nbformat_version]
Examples
--------
The simplest way to use nbconvert is
> jupyter nbconvert mynotebook.ipynb --to html
Options include ['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'rst', 'script', 'slides', 'webpdf'].
> jupyter nbconvert --to latex mynotebook.ipynb
Both HTML and LaTeX support multiple output templates. LaTeX includes
'base', 'article' and 'report'. HTML includes 'basic', 'lab' and
'classic'. You can specify the flavor of the format used.
> jupyter nbconvert --to html --template lab mynotebook.ipynb
You can also pipe the output to stdout, rather than a file
> jupyter nbconvert mynotebook.ipynb --stdout
PDF is generated via latex
> jupyter nbconvert mynotebook.ipynb --to pdf
You can get (and serve) a Reveal.js-powered slideshow
> jupyter nbconvert myslides.ipynb --to slides --post serve
Multiple notebooks can be given at the command line in a couple of
different ways:
> jupyter nbconvert notebook*.ipynb
> jupyter nbconvert notebook1.ipynb notebook2.ipynb
or you can specify the notebooks list in a config file, containing::
c.NbConvertApp.notebooks = ["my_notebook.ipynb"]
> jupyter nbconvert --config mycfg.py
To see all available configurables, use `--help-all`.
[NbConvertApp] WARNING | pattern 'Part_II_slide_deck_template.ipynb' matched no files